resources for data science.

resources – 6 oct 2018

As I mentioned in Balancing the Boat, I have been following the growth of Data Science for years. I never had the time to dive deep, but the desire was always there. Through the journey of learning about Data Science, I have gathered a number of resources. I hope that by gathering them together here in a one-stop-shop will not only help me, but maybe some others who are looking to learn more as well. My idea is that this post will evolve as my journey continues.

Twitter:

• My Twitter list
• kdnuggets
• PyData
• AndrewYNg
• analyticbridge (Data Science Central)
• KirkDBourne
• LinearDigressions
• kaggle
• IBMDataScience
• thisismetis (Metis)

Podcasts:

• Linear Digressions
• Partialy Derivative
• TWiML & AI
• Not So Standard Deviations
• Data Science Ethics

Blogs:

• Storytelling with Data
• Analytics Vidhya
• Towards Data Science

Books:

• Safari Books Online
• Python Data Science Handbook by Jake VanderPlas
• Storytelling With Data by Kole Nussbaumer Knaflic
• Data Science from Scratch: First Principles with Python by Joel Grus
• Weapons of Math Destruction by Cathy O’Neil

Online courses:

• Coursera: Data Science
• DataCamp

Meetups (these are local to Atlanta):

• PyData Atlanta
• PyAtl: Atlanta Python Programmers
• Data Science ATL

• Kaggle

Python libraries:

• NumPy
• pandas (Python Data Analysis Library)
• matplotlib
• altair
• seaborn
• bokeh
• scikitlearn
• tensorflow
• keras
• pytorch

Tools:

• Jupyter notebooks
• Atom editor
• Google Dataset Search
• GitHub

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